Path-Following Control of Autonomous Underwater Vehicles Subject to Velocity and Input Constraints via Neurodynamic Optimization
In this paper, a design method is presented for path-following control of underactuated autonomous underwater vehicles subject to velocity and input constraints, as well as internal and external disturbances. In the guidance loop, a kinematic control law of the desired surge speed and pitch rate is...
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| Veröffentlicht in: | IEEE transactions on industrial electronics (1982) 2019-11, Vol.66 (11), p.8724-8732 |
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| container_title | IEEE transactions on industrial electronics (1982) |
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| creator | Peng, Zhouhua Wang, Jun Han, Qing-Long |
| description | In this paper, a design method is presented for path-following control of underactuated autonomous underwater vehicles subject to velocity and input constraints, as well as internal and external disturbances. In the guidance loop, a kinematic control law of the desired surge speed and pitch rate is derived based on a backstepping technique and a line-of-sight guidance principle. In the control loop, an extended state observer is developed to estimate the extended state composed of unknown internal dynamics and external disturbances. Then, a disturbance rejection control law is constructed using the extended state observer. To bridge the guidance loop and the control loop, a reference governor is proposed for computing optimal guidance signals within the velocity and input constraints. The reference governor is formulated as a quadratically constrained optimization problem. A projection neural network is employed for solving the optimization problem in real time. Simulation results illustrate the effectiveness of the proposed method for path-following control of autonomous underwater vehicles subject to constraints and disturbances simultaneously in the vertical plane. |
| doi_str_mv | 10.1109/TIE.2018.2885726 |
| format | Article |
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In the guidance loop, a kinematic control law of the desired surge speed and pitch rate is derived based on a backstepping technique and a line-of-sight guidance principle. In the control loop, an extended state observer is developed to estimate the extended state composed of unknown internal dynamics and external disturbances. Then, a disturbance rejection control law is constructed using the extended state observer. To bridge the guidance loop and the control loop, a reference governor is proposed for computing optimal guidance signals within the velocity and input constraints. The reference governor is formulated as a quadratically constrained optimization problem. A projection neural network is employed for solving the optimization problem in real time. Simulation results illustrate the effectiveness of the proposed method for path-following control of autonomous underwater vehicles subject to constraints and disturbances simultaneously in the vertical plane.</description><identifier>ISSN: 0278-0046</identifier><identifier>EISSN: 1557-9948</identifier><identifier>DOI: 10.1109/TIE.2018.2885726</identifier><identifier>CODEN: ITIED6</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Autonomous underwater vehicles ; Autonomous underwater vehicles (AUVs) ; Bridge construction ; Computer simulation ; Constraints ; Control theory ; Design optimization ; Disturbances ; extended state observer (ESO) ; input and state constraints ; Kinematics ; Kinetic theory ; Neural networks ; neurodynamic optimization ; Neurodynamics ; Observers ; Optimization ; path following ; State observers ; Surges</subject><ispartof>IEEE transactions on industrial electronics (1982), 2019-11, Vol.66 (11), p.8724-8732</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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In the guidance loop, a kinematic control law of the desired surge speed and pitch rate is derived based on a backstepping technique and a line-of-sight guidance principle. In the control loop, an extended state observer is developed to estimate the extended state composed of unknown internal dynamics and external disturbances. Then, a disturbance rejection control law is constructed using the extended state observer. To bridge the guidance loop and the control loop, a reference governor is proposed for computing optimal guidance signals within the velocity and input constraints. The reference governor is formulated as a quadratically constrained optimization problem. A projection neural network is employed for solving the optimization problem in real time. Simulation results illustrate the effectiveness of the proposed method for path-following control of autonomous underwater vehicles subject to constraints and disturbances simultaneously in the vertical plane.</description><subject>Autonomous underwater vehicles</subject><subject>Autonomous underwater vehicles (AUVs)</subject><subject>Bridge construction</subject><subject>Computer simulation</subject><subject>Constraints</subject><subject>Control theory</subject><subject>Design optimization</subject><subject>Disturbances</subject><subject>extended state observer (ESO)</subject><subject>input and state constraints</subject><subject>Kinematics</subject><subject>Kinetic theory</subject><subject>Neural networks</subject><subject>neurodynamic optimization</subject><subject>Neurodynamics</subject><subject>Observers</subject><subject>Optimization</subject><subject>path following</subject><subject>State observers</subject><subject>Surges</subject><issn>0278-0046</issn><issn>1557-9948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMFLwzAUh4MoOKd3wUvAc2de0rTpcYxNB8MJbl5LlqYuo0tmkjrmyT_djg1PD977fr8HH0L3QAYApHhaTMcDSkAMqBA8p9kF6gHneVIUqbhEPUJzkRCSZtfoJoQNIZBy4D30-ybjOpm4pnF7Yz_xyNnoXYNdjYdtdNZtXRvw0lba72XUHn_otVGNDvi9XW20iji6btc4ZeIBS1vhqd218dgTopfGxoC_jcSvuvWuOli5NQrPd9FszY-MxtlbdFXLJui78-yj5WS8GL0ks_nzdDScJYoWEBMQlAoqGFM0rVaFkoylKltBTWkNjIs6lSmhXEDGqpzVdS5BMZ5lUAEtqkqwPno89e68-2p1iOXGtd52L0tKOUu5yBl0FDlRyrsQvK7LnTdb6Q8lkPLouew8l0fP5dlzF3k4RYzW-h_vbhwyyv4A44h6xA</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>Peng, Zhouhua</creator><creator>Wang, Jun</creator><creator>Han, Qing-Long</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-7207-0716</orcidid><orcidid>https://orcid.org/0000-0002-1305-5735</orcidid><orcidid>https://orcid.org/0000-0003-4468-7281</orcidid></search><sort><creationdate>20191101</creationdate><title>Path-Following Control of Autonomous Underwater Vehicles Subject to Velocity and Input Constraints via Neurodynamic Optimization</title><author>Peng, Zhouhua ; Wang, Jun ; Han, Qing-Long</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-182282833c24db9ca334c6b1f22f1358f4a40258163d73ff7a1c35661d129dd83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Autonomous underwater vehicles</topic><topic>Autonomous underwater vehicles (AUVs)</topic><topic>Bridge construction</topic><topic>Computer simulation</topic><topic>Constraints</topic><topic>Control theory</topic><topic>Design optimization</topic><topic>Disturbances</topic><topic>extended state observer (ESO)</topic><topic>input and state constraints</topic><topic>Kinematics</topic><topic>Kinetic theory</topic><topic>Neural networks</topic><topic>neurodynamic optimization</topic><topic>Neurodynamics</topic><topic>Observers</topic><topic>Optimization</topic><topic>path following</topic><topic>State observers</topic><topic>Surges</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Peng, Zhouhua</creatorcontrib><creatorcontrib>Wang, Jun</creatorcontrib><creatorcontrib>Han, Qing-Long</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on industrial electronics (1982)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Peng, Zhouhua</au><au>Wang, Jun</au><au>Han, Qing-Long</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Path-Following Control of Autonomous Underwater Vehicles Subject to Velocity and Input Constraints via Neurodynamic Optimization</atitle><jtitle>IEEE transactions on industrial electronics (1982)</jtitle><stitle>TIE</stitle><date>2019-11-01</date><risdate>2019</risdate><volume>66</volume><issue>11</issue><spage>8724</spage><epage>8732</epage><pages>8724-8732</pages><issn>0278-0046</issn><eissn>1557-9948</eissn><coden>ITIED6</coden><abstract>In this paper, a design method is presented for path-following control of underactuated autonomous underwater vehicles subject to velocity and input constraints, as well as internal and external disturbances. In the guidance loop, a kinematic control law of the desired surge speed and pitch rate is derived based on a backstepping technique and a line-of-sight guidance principle. In the control loop, an extended state observer is developed to estimate the extended state composed of unknown internal dynamics and external disturbances. Then, a disturbance rejection control law is constructed using the extended state observer. To bridge the guidance loop and the control loop, a reference governor is proposed for computing optimal guidance signals within the velocity and input constraints. The reference governor is formulated as a quadratically constrained optimization problem. A projection neural network is employed for solving the optimization problem in real time. 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| ispartof | IEEE transactions on industrial electronics (1982), 2019-11, Vol.66 (11), p.8724-8732 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Autonomous underwater vehicles Autonomous underwater vehicles (AUVs) Bridge construction Computer simulation Constraints Control theory Design optimization Disturbances extended state observer (ESO) input and state constraints Kinematics Kinetic theory Neural networks neurodynamic optimization Neurodynamics Observers Optimization path following State observers Surges |
| title | Path-Following Control of Autonomous Underwater Vehicles Subject to Velocity and Input Constraints via Neurodynamic Optimization |
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